标题： 基因表达分布偏移的建模用于未见的遗传扰动 显示英文标题

标题： Modeling Gene Expression Distributional Shifts for Unseen Genetic Perturbations

摘要： 我们训练一个神经网络来预测基因表达在基因扰动后的分布响应。 这是一个在早期药物发现阶段的重要任务，因为这些响应可以提供关于基因功能的见解并指导靶点识别。 现有方法仅预测平均表达的变化，忽略了单细胞数据中固有的随机性。 相反，我们通过建模表达分布来提供更真实的细胞响应视图。 我们的模型根据扰动预测基因级别的直方图，并在以较少的训练成本捕捉高阶统计特性（如方差、偏度和峰度）方面优于基线方法。 为了推广到未见过的扰动，我们通过来自大型语言模型（LLMs）的基因嵌入来整合先验知识。 尽管建模了更丰富的输出空间，该方法在预测平均表达变化方面仍具有竞争力。 这项工作为更丰富且具有生物学信息的扰动效应模型提供了一个实用的步骤。 显示英文摘要

摘要： We train a neural network to predict distributional responses in gene expression following genetic perturbations. This is an essential task in early-stage drug discovery, where such responses can offer insights into gene function and inform target identification. Existing methods only predict changes in the mean expression, overlooking stochasticity inherent in single-cell data. In contrast, we offer a more realistic view of cellular responses by modeling expression distributions. Our model predicts gene-level histograms conditioned on perturbations and outperforms baselines in capturing higher-order statistics, such as variance, skewness, and kurtosis, at a fraction of the training cost. To generalize to unseen perturbations, we incorporate prior knowledge via gene embeddings from large language models (LLMs). While modeling a richer output space, the method remains competitive in predicting mean expression changes. This work offers a practical step towards more expressive and biologically informative models of perturbation effects.